Verbs,Nouns,Adjs

Summary Stats

agr <- d %>% 
  group_by(Category,Task) %>% 
  summarize(MeanAccuracy = mean(Accuracy),
            SD = sd(Accuracy))

## `summarise()` has grouped output by 'Category'. You can override using the
## `.groups` argument.

print(agr)

## # A tibble: 7 × 4
## # Groups:   Category [3]
##   Category Task     MeanAccuracy    SD
##   <chr>    <chr>           <dbl> <dbl>
## 1 Adjs     Concrete        0.751 0.432
## 2 Adjs     Valence         0.934 0.248
## 3 Nouns    Animacy         0.965 0.183
## 4 Nouns    Concrete        0.871 0.335
## 5 Nouns    Valence         0.931 0.253
## 6 Verbs    Concrete        0.917 0.276
## 7 Verbs    Valence         0.956 0.205

Start with Accuracy

agr <- d %>% 
  group_by(Task) %>% 
  reframe(MeanAccuracy = mean(Accuracy), 
          CILow = ci.low(Accuracy), 
          CIHigh = ci.high(Accuracy)) %>%
  mutate(YMin = MeanAccuracy - CILow, 
         YMax = MeanAccuracy + CIHigh)
# View(agr)

dodge = position_dodge(.9)
ggplot(data=agr, aes(x=Task,y=MeanAccuracy, fill = Task)) +
  geom_bar(position=dodge,stat="identity") + 
  geom_errorbar(aes(ymin=YMin,ymax=YMax),width=.25,position=position_dodge(0.9))

  # theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
  # guides(fill = "none")

agr <- d %>% 
  group_by(Task,Category) %>% 
  reframe(MeanAccuracy = mean(Accuracy), 
          CILow = ci.low(Accuracy), 
          CIHigh = ci.high(Accuracy)) %>%
  mutate(YMin = MeanAccuracy - CILow, 
         YMax = MeanAccuracy + CIHigh)
# View(agr)

dodge = position_dodge(.9)
ggplot(data=agr, aes(x=Task,y=MeanAccuracy, fill = Category)) +
  geom_bar(position=dodge,stat="identity") + 
  geom_errorbar(aes(ymin=YMin,ymax=YMax),width=.25,position=position_dodge(0.9))

  # theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
  # guides(fill = "none")

agr <- d %>% 
  group_by(Word,Category,Task) %>%
  summarize(MeanAccuracy = mean(Accuracy),
            MeanReactionTime = mean(ReactionTime))

## `summarise()` has grouped output by 'Word', 'Category'. You can override using
## the `.groups` argument.

ggplot(agr, aes(x = MeanReactionTime, y = MeanAccuracy)) +
  geom_point() +
  geom_smooth(method = "lm", se = FALSE, color = "black") +
  geom_text(aes(label = Word, color = Category), vjust = -0.5, hjust = 1.5) +
  facet_wrap(~Task)

## `geom_smooth()` using formula = 'y ~ x'

  # guides(legend = "none")
  # theme(legend.position = "none")  # Remove the legend
# ggsave("../graphs/exp1b_accXrt.pdf",width = 5, height = 3)

By Participant

agr <- d %>%
    group_by(ID.true,Task) %>%
    reframe(MeanAccuracy = mean(Accuracy), 
            CILow = ci.low(Accuracy), 
            CIHigh = ci.high(Accuracy)) %>%
    mutate(YMin = MeanAccuracy - CILow, 
           YMax = MeanAccuracy + CIHigh)

dodge = position_dodge(.9)
ggplot(data=agr, aes(x=reorder(ID.true,MeanAccuracy),y=MeanAccuracy,fill=Task)) +
  geom_bar(position=dodge,stat="identity") +
  facet_wrap(~Task) +
  geom_errorbar(aes(ymin=YMin,ymax=YMax),width=.25,position=position_dodge(0.9)) +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

  # guides(fill = "none")

ReactionTime by Task

agr <- d %>% 
  group_by(Word,Task,Category) %>% 
  reframe(MeanAccuracy = mean(Accuracy), 
          CILow = ci.low(Accuracy), 
          CIHigh = ci.high(Accuracy)) %>%
  mutate(YMin = MeanAccuracy - CILow, 
         YMax = MeanAccuracy + CIHigh)

ggplot(agr, aes(x=Task, y=MeanAccuracy,fill=Category)) + 
    geom_violin(trim=FALSE,alpha=.4) +
    geom_jitter(shape=16, position=position_jitter(0.2))

  # guides(fill = "none")

By Participant

agr <- d %>%
    group_by(ID.true,Task,ConcValCombo) %>%
    summarize(MeanReactionTime = mean(ReactionTime), 
              CILow = ci.low(ReactionTime), 
              CIHigh = ci.high(ReactionTime)) %>%
    mutate(YMin = MeanReactionTime - CILow, 
           YMax = MeanReactionTime + CIHigh)

## `summarise()` has grouped output by 'ID.true', 'Task'. You can override using
## the `.groups` argument.

dodge = position_dodge(.9)
ggplot(data=agr, aes(x=reorder(ID.true,MeanReactionTime),y=MeanReactionTime,fill=ConcValCombo)) +
  geom_bar(position=dodge,stat="identity") +
  facet_wrap(~Task) +
  geom_errorbar(aes(ymin=YMin,ymax=YMax),width=.25,position=position_dodge(0.9)) +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

  # guides(fill = "none")

Looking at Responses Generally

Proportion Concrete

agr <- d %>% 
  filter(Task == "Concrete") %>% 
  mutate(Response.n = as.numeric(factor(Response, levels = c("abstract", "concrete"))) - 1) %>% 
  group_by(ConcValCombo,Category) %>%
  summarize(PropConcrete = mean(Response.n),
          CILow = ci.low(Response.n), 
          CIHigh = ci.high(Response.n)) %>%
  mutate(YMin = PropConcrete - CILow, 
          YMax = PropConcrete + CIHigh)

## `summarise()` has grouped output by 'ConcValCombo'. You can override using the
## `.groups` argument.

dodge = position_dodge(.9)
ggplot(data=agr, aes(x=Category,y=PropConcrete,fill=ConcValCombo)) +
  geom_bar(position=dodge,stat="identity") +
  # facet_wrap(~Version) +
  geom_errorbar(aes(ymin=YMin,ymax=YMax),width=.25,position=position_dodge(0.9))

  # theme(axis.text.x = element_text(angle = 45, hjust = 1))

agr <- d %>% 
  filter(Task == "Concrete") %>% 
  mutate(Response.n = as.numeric(factor(Response, levels = c("abstract", "concrete"))) - 1) %>% 
  group_by(Word,ConcValCombo) %>%
  summarize(PropConcrete = mean(Response.n),
            MeanReactionTime = mean(ReactionTime))

## `summarise()` has grouped output by 'Word'. You can override using the
## `.groups` argument.

ggplot(agr, aes(x = MeanReactionTime, y = PropConcrete)) +
  geom_point() +
  geom_smooth(method = "lm", se = FALSE, color = "red") +
  geom_text(aes(label = Word, color = ConcValCombo), vjust = -0.5, hjust = 1.5) 

## `geom_smooth()` using formula = 'y ~ x'

  # geom_text_repel(aes(label = Word, color = ConcValCombo), 
                  # vjust = -0.5, hjust = 1.5) +
  # scale_fill_manual(values=cbPalette) +
  # scale_color_manual(values=cbPalette)

agr <- d %>% 
  filter(Task == "Valence") %>% 
  mutate(Response.n = as.numeric(factor(Response, levels = c("negative", "positive"))) - 1) %>% 
  group_by(Word,ConcValCombo) %>%
  summarize(PropPositive = mean(Response.n),
            MeanReactionTime = mean(ReactionTime))

## `summarise()` has grouped output by 'Word'. You can override using the
## `.groups` argument.

ggplot(agr, aes(x = MeanReactionTime, y = PropPositive)) +
  geom_point() +
  geom_smooth(method = "lm", se = FALSE, color = "red") +
  geom_text(aes(label = Word, color = ConcValCombo), vjust = -0.5, hjust = 1.5) 

## `geom_smooth()` using formula = 'y ~ x'

  # geom_text_repel(aes(label = Word, color = ConcValCombo), 
                  # vjust = -0.5, hjust = 1.5) +
  # scale_fill_manual(values=cbPalette) +
  # scale_color_manual(values=cbPalette)

agr <- d %>% 
  filter(Task == "Animacy") %>% 
  mutate(Response.n = as.numeric(factor(Response, levels = c("inanimate", "animate"))) - 1) %>% 
  group_by(Word,Animacy,Valence) %>%
  summarize(PropAnimate = mean(Response.n),
            MeanReactionTime = mean(ReactionTime))

## `summarise()` has grouped output by 'Word', 'Animacy'. You can override using
## the `.groups` argument.

ggplot(agr, aes(x = MeanReactionTime, y = PropAnimate)) +
  geom_point() +
  geom_smooth(method = "lm", se = FALSE, color = "red") +
  geom_text(aes(label = Word, color = Valence), vjust = -0.5, hjust = 1.5)

## `geom_smooth()` using formula = 'y ~ x'

  # geom_text_repel(aes(label = Word, color = ConcValCombo), 
                  # vjust = -0.5, hjust = 1.5) +
  # scale_fill_manual(values=cbPalette) +
  # scale_color_manual(values=cbPalette)

agr <- d %>% 
  # filter(Task == "Concrete") %>% 
  # mutate(Response.n = as.numeric(factor(Response, levels = c("abstract", "concrete"))) - 1) %>% 
  group_by(Word,Task,Category) %>%
  summarize(MeanAccuracy = mean(Accuracy),
            MeanReactionTime = mean(ReactionTime))

## `summarise()` has grouped output by 'Word', 'Task'. You can override using the
## `.groups` argument.

ggplot(agr, aes(x = MeanReactionTime, y = MeanAccuracy)) +
  geom_point() +
  geom_smooth(method = "lm", se = FALSE, color = "black") +
  facet_wrap(~Task) +
  geom_text(aes(label = Word, color = Category), vjust = -0.5, hjust = 1.5) +
  guides(legend = "none")

## `geom_smooth()` using formula = 'y ~ x'

  #   theme(
  #   legend.position = "top",    # Move legend to the top
  #   legend.title = element_text(size = 10),  # Adjust legend title size
  #   legend.text = element_text(size = 9)     # Adjust legend text size
  # )
  

  # geom_text_repel(aes(label = Word, color = ConcValCombo), 
                  # vjust = -0.5, hjust = 1.5) +
  # scale_fill_manual(values=cbPalette) +
  # scale_color_manual(values=cbPalette)

# ggsave("../graphs/exp3_accXrt.pdf",width = 5, height = 3)

Reaction Time analyses

Data Cleaning

# First remove inaccurate participants
length(unique(d$ID.true))

## [1] 118

inacc.parts <- d %>% 
  group_by(ID.true,Task,Category) %>% 
  summarise(MeanAccuracy = mean(Accuracy)) %>% 
  filter(MeanAccuracy < .75)

## `summarise()` has grouped output by 'ID.true', 'Task'. You can override using
## the `.groups` argument.

# How many participants have Accuracy < .75?
length(unique(inacc.parts$ID.true))

## [1] 23

# Remove them
d.inaccurate.removed <- d %>% 
  anti_join(inacc.parts, by = "ID.true")
# Sanity check
length(unique(d.inaccurate.removed$ID.true))

## [1] 95

# Second, remove all inaccurate trials
orig <- nrow(d.inaccurate.removed)
d.inaccurate.removed <- d.inaccurate.removed %>% 
  filter(Accuracy == 1)
nrow(d.inaccurate.removed)/orig*100

## [1] 94.05759

# Third, Remove subjects with ReactionTime higher than 3x IQR
summary(d.inaccurate.removed$LogReactionTime)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   2.303   6.426   6.608   6.694   6.872  10.619

  #  Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  # 6.924   7.328   7.436   7.479   7.579  10.008 
range(d.inaccurate.removed$LogReactionTime)

## [1]  2.302585 10.618714

hist(d.inaccurate.removed$LogReactionTime, breaks=100, col="lightblue", xlab="LogReactionTime (ms)",
        main="Histogram with Normal Curve")

quantile(d.inaccurate.removed$LogReactionTime, na.rm = TRUE)

##        0%       25%       50%       75%      100% 
##  2.302585  6.426488  6.608001  6.872128 10.618714

IQR(d.inaccurate.removed$LogReactionTime, na.rm = TRUE)*3 # 0.7526289

## [1] 1.336919

cutoff.high <- quantile(d.inaccurate.removed$LogReactionTime, na.rm = TRUE)[4] + IQR(d.inaccurate.removed$LogReactionTime, na.rm = TRUE)*3 # 8.419261
cutoff.low <- quantile(d.inaccurate.removed$LogReactionTime, na.rm = TRUE)[2] - IQR(d.inaccurate.removed$LogReactionTime, na.rm = TRUE)*3# 6.5088838.419261


# remove subjects with ReactionTime higher than 3 x IQR
df.outliers.removed <- subset(d.inaccurate.removed, (d.inaccurate.removed$LogReactionTime > cutoff.low) & (d.inaccurate.removed$LogReactionTime < cutoff.high))

hist(df.outliers.removed$LogReactionTime, breaks=100, col="lightblue", xlab="LogReactionTime (ms)",
        main="Histogram with Normal Curve")

ggplot(df.outliers.removed, aes(x=LogReactionTime, fill=Task)) +
  # facet_wrap(~BlockOrder) +
  geom_density(alpha = .4)

ggplot(df.outliers.removed, aes(x=ReactionTime, fill=Task)) +
  # facet_wrap(~BlockOrder) +
  geom_density(alpha = .4)

Summary Stats

agr <- d.inaccurate.removed %>% 
  group_by(Task,Category) %>% 
  summarize(MeanRT = mean(ReactionTime),
            SD = sd(ReactionTime),
            MeanLogRT = mean(LogReactionTime))

## `summarise()` has grouped output by 'Task'. You can override using the
## `.groups` argument.

print(agr)

## # A tibble: 7 × 5
## # Groups:   Task [3]
##   Task     Category MeanRT    SD MeanLogRT
##   <chr>    <chr>     <dbl> <dbl>     <dbl>
## 1 Animacy  Nouns      831.  483.      6.63
## 2 Concrete Adjs      1012.  667.      6.81
## 3 Concrete Nouns      934.  631.      6.74
## 4 Concrete Verbs     1071. 1091.      6.85
## 5 Valence  Adjs       780.  350.      6.60
## 6 Valence  Nouns      834.  669.      6.63
## 7 Valence  Verbs      866.  406.      6.69

LogReactionTime by Task

agr <- df.outliers.removed %>%
    group_by(Task,Word) %>%
    summarize(MeanLogReactionTime = mean(LogReactionTime), 
              CILow = ci.low(LogReactionTime), 
              CIHigh = ci.high(LogReactionTime)) %>%
    mutate(YMin = MeanLogReactionTime - CILow, 
           YMax = MeanLogReactionTime + CIHigh)

## `summarise()` has grouped output by 'Task'. You can override using the
## `.groups` argument.

ggplot(agr, aes(x=MeanLogReactionTime, fill=Task)) +
  geom_density(alpha = .4)

ggplot(agr, aes(x=Task, y=MeanLogReactionTime,fill=Task)) + 
    geom_violin(trim=FALSE,alpha=.4) +
    geom_jitter(shape=16, position=position_jitter(0.2)) +
    # geom_errorbar(aes(ymin=YMin,ymax=YMax),width=.25,position="dodge", show.legend = FALSE) +
  # theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
  guides(fill = "none")

ReactionTime by Task

agr <- df.outliers.removed %>%
    group_by(Task,Word) %>%
    summarize(MeanReactionTime = mean(ReactionTime), 
              CILow = ci.low(ReactionTime), 
              CIHigh = ci.high(ReactionTime)) %>%
    mutate(YMin = MeanReactionTime - CILow, 
           YMax = MeanReactionTime + CIHigh)

## `summarise()` has grouped output by 'Task'. You can override using the
## `.groups` argument.

ggplot(agr, aes(x=MeanReactionTime, fill=Task)) +
  geom_density(alpha = .4)

ggplot(agr, aes(x=Task, y=MeanReactionTime,fill=Task)) + 
    geom_violin(trim=FALSE,alpha=.4) +
    geom_jitter(shape=16, position=position_jitter(0.2)) +
    # geom_errorbar(aes(ymin=YMin,ymax=YMax),width=.25,position="dodge", show.legend = FALSE) +
  # theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
  guides(fill = "none")

# ggsave("../graphs/total_rt_violin.pdf",width = 4, height = 3)

Prop Conc/Val

prop <- d %>% 
  filter(Task == "Concrete") %>% 
  mutate(Response.n = as.numeric(factor(Response, levels = c("abstract", "concrete"))) - 1) %>% 
  group_by(Word) %>%
  summarize(PropConcrete = mean(Response.n))


agr <- df.outliers.removed %>% 
  filter(Task == "Concrete") %>% 
  mutate(Response.n = as.numeric(factor(Response, levels = c("abstract", "concrete"))) - 1) %>% 
  group_by(Word,ConcValCombo) %>%
  summarize(PropConcrete = mean(Response.n),
            MeanReactionTime = mean(ReactionTime))

## `summarise()` has grouped output by 'Word'. You can override using the
## `.groups` argument.

ggplot(agr, aes(x = MeanReactionTime, y = PropConcrete)) +
  geom_point() +
  geom_smooth(method = "lm", se = FALSE, color = "red") +
  geom_text(aes(label = Word,color=ConcValCombo), vjust = -0.5, hjust = 1.5) 

## `geom_smooth()` using formula = 'y ~ x'

  # geom_text_repel(aes(label = Word, color = ConcValCombo), 
                  # vjust = -0.5, hjust = 1.5) +
  # scale_fill_manual(values=cbPalette) +
  # scale_color_manual(values=cbPalette)

Does REsponse choice influence RT??

Valence

Yes, looks like choosing negative is faster than choosing positive d

Concrete

just barely, choosing abstract has a negative effect on RT

Animacy

Nope.

Category

agr <- df.outliers.removed %>%
    group_by(Task,Category) %>%
    reframe(MeanReactionTime = mean(ReactionTime), 
            CILow = ci.low(ReactionTime), 
            CIHigh = ci.high(ReactionTime)) %>%
    mutate(YMin = MeanReactionTime - CILow, 
           YMax = MeanReactionTime + CIHigh)

dodge = position_dodge(.9)
ggplot(data=agr, aes(x=reorder(Category,MeanReactionTime),y=MeanReactionTime,fill=Task)) +
  geom_bar(position=dodge,stat="identity") +
  # facet_wrap(~Task) +
  geom_errorbar(aes(ymin=YMin,ymax=YMax),width=.25,position=position_dodge(0.9)) +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

  # guides(fill = "none")

ReactionTime by Task

agr <- df.outliers.removed %>%
    group_by(Word,Task,Category) %>%
    reframe(MeanReactionTime = mean(ReactionTime), 
            CILow = ci.low(ReactionTime), 
            CIHigh = ci.high(ReactionTime)) %>%
    mutate(YMin = MeanReactionTime - CILow, 
           YMax = MeanReactionTime + CIHigh)

ggplot(agr, aes(x=Category, y=MeanReactionTime,fill=Task)) + 
    geom_violin(trim=FALSE,alpha=.4) +
    geom_jitter(shape=16, position=position_jitter(0.2)) +
    guides(fill = "none")

Look at cases where RT Conc <= RT Valence or Conc Acc >= Val Acc

test <- df.outliers.removed %>%
  # filter(Task %in% c("Concrete", "Valence")) %>%  # Keep only relevant tasks
  group_by(Word, Task) %>%
  summarise(
    RT = mean(ReactionTime, na.rm = TRUE),  # Take the mean RT if duplicates exist
    .groups = "drop_last" # Drop grouping by Task but keep Word and ID.true
  ) %>%
  pivot_wider(names_from = Task, values_from = RT, names_prefix = "RT_") %>% # Reshape to wide format
  filter((RT_Concrete <= RT_Valence) | (RT_Animacy <= RT_Valence)) %>%  # Apply the condition
    pivot_longer(
    cols = starts_with("RT_"), # Select the reshaped columns
    names_to = "Task",         # Restore Task column
    names_prefix = "RT_",      # Remove "RT_" prefix to match original Task names
    values_to = "ReactionTime" # Column for the RT values
  ) %>%
  ungroup()

nrow(test)/nrow(df.outliers.removed)

## [1] 0.005041311

agr <- test %>% 
    group_by(Word, Task) %>%
    summarize(MeanReactionTime = mean(ReactionTime), 
            CILow = ci.low(ReactionTime), 
            CIHigh = ci.high(ReactionTime)) %>%
    mutate(YMin = MeanReactionTime - CILow, 
           YMax = MeanReactionTime + CIHigh)

## `summarise()` has grouped output by 'Word'. You can override using the
## `.groups` argument.

dodge = position_dodge(.9)
ggplot(data=agr, aes(x=Task,y=MeanReactionTime,fill=Task)) +
  geom_bar(position=dodge,stat="identity") +
  facet_wrap(~Word) +
  geom_errorbar(aes(ymin=YMin,ymax=YMax),width=.25,position=position_dodge(0.9)) +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

## Warning: Removed 29 rows containing missing values or values outside the scale range
## (`geom_bar()`).

  # guides(fill = "none")

ggplot(agr,aes(x=Word, y=MeanReactionTime, alpha=Task, fill=Task)) +
  geom_bar(position="dodge",stat="identity") +
  geom_errorbar(aes(ymin=YMin,ymax=YMax),width=.25,position=position_dodge(0.9)) +
  # facet_wrap(~Task, ncol=10) +
  xlab("ConcValCombo") +
  ylab("MeanAccuracy") +
  guides(fill=FALSE) +
  # guides(alpha=guide_legend(title="Task")) +
  theme(legend.key.size = unit(0.3, "cm"),
        legend.position = "top", # c(.5,1)
        legend.direction = "horizontal",
        legend.margin=margin(0,0,0,0),
        legend.box.margin=margin(0,0,-5,-5),legend.spacing.y = unit(0.001, 'cm')) +
    # scale_fill_manual(values=cbPalette) +
    # scale_color_manual(values=cbPalette) +
    scale_alpha_discrete(range = c(.5,1)) +
    theme(axis.text.x = element_text(angle = 45, hjust = 1))

## Warning: The `<scale>` argument of `guides()` cannot be `FALSE`. Use "none" instead as
## of ggplot2 3.3.4.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.

## Warning: Using alpha for a discrete variable is not advised.

## Warning: Removed 29 rows containing missing values or values outside the scale range
## (`geom_bar()`).

test_avv <- d %>% 
  filter(Word %in% test$Word) %>% 
  group_by(Word, Task) %>%
    summarize(MeanAccuracy = mean(Accuracy),
            CILow = ci.low(Accuracy), 
            CIHigh = ci.high(Accuracy),
            ) %>%
    mutate(YMin = MeanAccuracy - CILow, 
           YMax = MeanAccuracy + CIHigh)

## `summarise()` has grouped output by 'Word'. You can override using the
## `.groups` argument.

# View(test_avv)

dodge = position_dodge(.9)
ggplot(data=test_avv, aes(x=Task,y=MeanAccuracy,fill=Task)) +
  geom_bar(position=dodge,stat="identity") +
  facet_wrap(~Word) +
  geom_errorbar(aes(ymin=YMin,ymax=YMax),width=.25,position=position_dodge(0.9)) +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

  # guides(fill = "none")

test_avv <- d %>% 
  filter(Word %in% test$Word) %>% 
  group_by(Word, Task) %>%
    summarize(MeanAccuracy = mean(Accuracy),
              MeanReactionTime = mean(ReactionTime))

## `summarise()` has grouped output by 'Word'. You can override using the
## `.groups` argument.

ggplot(test_avv, aes(x = MeanReactionTime, y = MeanAccuracy)) +
  geom_point() +
  geom_smooth(method = "lm", se = FALSE, color = "red") +
  # geom_text(aes(label = Word, color = Task), vjust = -0.5, hjust = 1.5)
  geom_text_repel(aes(label = Word, color = Task), 
                  vjust = -0.5, hjust = 1.5) +
  scale_fill_manual(values=cbPalette) +
  scale_color_manual(values=cbPalette)

## `geom_smooth()` using formula = 'y ~ x'

## Warning: ggrepel: 36 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps

Conc Acc is higher than Val Acc

# Compute highest accuracy for Concrete
concrete_accuracy <- d %>% 
  group_by(Word,Task) %>%
  summarize(MeanAccuracy = mean(Accuracy),
            MeanReactionTime = mean(ReactionTime)) %>% 
  filter(Task == "Concrete") %>%
  select(Word, MeanAccuracy) %>%
  rename(ConcreteAccuracy = MeanAccuracy) %>% 
  arrange(desc(ConcreteAccuracy)) %>% 
  head(10)

## `summarise()` has grouped output by 'Word'. You can override using the
## `.groups` argument.

agr <- df.outliers.removed %>% 
  filter(Word %in% concrete_accuracy$Word) %>% 
  group_by(Word,Task) %>%
  summarize(MeanAccuracy = mean(Accuracy),
            MeanReactionTime = mean(ReactionTime))

## `summarise()` has grouped output by 'Word'. You can override using the
## `.groups` argument.

print(agr)

## # A tibble: 20 × 4
## # Groups:   Word [10]
##    Word        Task     MeanAccuracy MeanReactionTime
##    <chr>       <chr>           <dbl>            <dbl>
##  1 Despairing  Concrete            1             929.
##  2 Despairing  Valence             1             706.
##  3 Hopeless    Concrete            1             882.
##  4 Hopeless    Valence             1             722.
##  5 chocolate   Concrete            1             747.
##  6 chocolate   Valence             1             697.
##  7 despair     Concrete            1             818.
##  8 despair     Valence             1             746.
##  9 grief       Concrete            1             861.
## 10 grief       Valence             1             769.
## 11 inspiration Concrete            1             800.
## 12 inspiration Valence             1             782.
## 13 kindness    Concrete            1             947.
## 14 kindness    Valence             1             635.
## 15 kiss        Concrete            1             877.
## 16 kiss        Valence             1             831.
## 17 mud         Concrete            1             906.
## 18 mud         Valence             1             770.
## 19 shame       Concrete            1             849.
## 20 shame       Valence             1             705.

ggplot(agr, aes(x = MeanReactionTime, y = MeanAccuracy)) +
  geom_point() +
  geom_smooth(method = "lm", se = FALSE, color = "black") +
  geom_text(aes(label = Word, color = Task), vjust = -0.5, hjust = 1.5)

## `geom_smooth()` using formula = 'y ~ x'

  # guides(legend = "none")
  # theme(legend.position = "none")  # Remove the legend
# ggsave("../graphs/exp1b_accXrt.pdf",width = 5, height = 3)

agr <- df.outliers.removed %>% 
    filter(Word %in% concrete_accuracy$Word) %>% 
    group_by(Word, Task) %>%
    summarize(MeanReactionTime = mean(ReactionTime), 
            CILow = ci.low(ReactionTime), 
            CIHigh = ci.high(ReactionTime)) %>%
    mutate(YMin = MeanReactionTime - CILow, 
           YMax = MeanReactionTime + CIHigh)

## `summarise()` has grouped output by 'Word'. You can override using the
## `.groups` argument.

print(agr)

## # A tibble: 20 × 7
## # Groups:   Word [10]
##    Word        Task     MeanReactionTime CILow CIHigh  YMin  YMax
##    <chr>       <chr>               <dbl> <dbl>  <dbl> <dbl> <dbl>
##  1 Despairing  Concrete             929. 161.   188.   768. 1116.
##  2 Despairing  Valence              706.  53.4   54.4  653.  760.
##  3 Hopeless    Concrete             882. 140.   162.   742. 1043.
##  4 Hopeless    Valence              722.  64.0   62.8  658.  785.
##  5 chocolate   Concrete             747.  64.4   67.5  682.  814.
##  6 chocolate   Valence              697.  48.2   49.2  649.  747.
##  7 despair     Concrete             818. 105.   128.   713.  946.
##  8 despair     Valence              746.  67.4   74.4  678.  820.
##  9 grief       Concrete             861.  97.5  106.   764.  967.
## 10 grief       Valence              769.  89.4  117.   680.  886.
## 11 inspiration Concrete             800.  65.6   72.8  735.  873.
## 12 inspiration Valence              782.  67.9   78.2  714.  860.
## 13 kindness    Concrete             947. 126.   160.   821. 1107.
## 14 kindness    Valence              635.  32.8   36.8  602.  671.
## 15 kiss        Concrete             877. 106.   122.   771.  999.
## 16 kiss        Valence              831.  89.9  114.   741.  945.
## 17 mud         Concrete             906. 117.   130.   789. 1036.
## 18 mud         Valence              770.  88.7   91.4  681.  861.
## 19 shame       Concrete             849.  81.1   97.1  768.  946.
## 20 shame       Valence              705.  43.9   49.5  661.  755.

dodge = position_dodge(.9)
ggplot(data=agr, aes(x=Word,y=MeanReactionTime,fill=Task)) +
  geom_bar(position=dodge,stat="identity") +
  # facet_wrap(~Word) +
  geom_errorbar(aes(ymin=YMin,ymax=YMax),width=.25,position=position_dodge(0.9)) +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

  # guides(fill = "none")

agr <- df.outliers.removed %>%
  filter(Word %in% concrete_accuracy$Word)

ggplot(agr, aes(x=Word, y=ReactionTime,fill=Task)) + 
    geom_violin(trim=FALSE,alpha=.4) +
    # Median dot
    stat_summary(fun = median, geom = "point", 
                 shape = 21, size = 1.5, 
                 position = position_dodge(width=0.9)) +  # Centering the median dot
    # geom_jitter(shape=10, position=position_jitter(0.2)) +
    theme(axis.text.x = element_text(angle = 45, hjust = 1))

    # guides(fill = "none")

Analysis

convert everything to factors

Is there a difference between Semantic and Valence Tasks?

Yes

m = lmer(LogReactionTime ~ Task + (1|ID.true) + (1|Word), data=center)
summary(m)

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: LogReactionTime ~ Task + (1 | ID.true) + (1 | Word)
##    Data: center
## 
## REML criterion at convergence: 9878.4
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.4568 -0.6512 -0.1716  0.4472  5.3564 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  Word     (Intercept) 0.002692 0.05189 
##  ID.true  (Intercept) 0.032749 0.18097 
##  Residual             0.089738 0.29956 
## Number of obs: 21423, groups:  Word, 226; ID.true, 95
## 
## Fixed effects:
##                Estimate Std. Error         df t value Pr(>|t|)    
## (Intercept)   6.672e+00  2.086e-02  1.450e+02 319.815  < 2e-16 ***
## TaskConcrete  9.314e-02  1.032e-02  2.105e+04   9.021  < 2e-16 ***
## TaskValence  -4.412e-02  9.279e-03  2.116e+04  -4.755 1.99e-06 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) TskCnc
## TaskConcret -0.399       
## TaskValence -0.402  0.897

table(center$Category)

## 
##  Adjs Nouns Verbs 
##  5340 11815  4268

m = lmer(LogReactionTime ~ Category + (1|ID.true) + (1|Word), data=center)
summary(m)

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: LogReactionTime ~ Category + (1 | ID.true) + (1 | Word)
##    Data: center
## 
## REML criterion at convergence: 10771.5
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.1545 -0.6492 -0.1881  0.4500  5.2729 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  Word     (Intercept) 0.002583 0.05082 
##  ID.true  (Intercept) 0.033085 0.18189 
##  Residual             0.093664 0.30605 
## Number of obs: 21423, groups:  Word, 226; ID.true, 95
## 
## Fixed effects:
##               Estimate Std. Error       df t value Pr(>|t|)    
## (Intercept)    6.68979    0.03781 96.26099 176.917   <2e-16 ***
## CategoryNouns -0.02990    0.04579 96.91688  -0.653    0.515    
## CategoryVerbs  0.06257    0.05702 97.15462   1.097    0.275    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) CtgryN
## CategoryNns -0.826       
## CategryVrbs -0.663  0.549

m = lmer(LogReactionTime ~ Task*Category + (1|ID.true) + (1|Word), data=center)

## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients

summary(m)

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: LogReactionTime ~ Task * Category + (1 | ID.true) + (1 | Word)
##    Data: center
## 
## REML criterion at convergence: 9808.1
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.4766 -0.6492 -0.1704  0.4463  5.3271 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  Word     (Intercept) 0.002679 0.05175 
##  ID.true  (Intercept) 0.032848 0.18124 
##  Residual             0.089362 0.29893 
## Number of obs: 21423, groups:  Word, 226; ID.true, 95
## 
## Fixed effects:
##                              Estimate Std. Error         df t value Pr(>|t|)
## (Intercept)                 6.642e+00  3.901e-02  1.106e+02 170.271  < 2e-16
## TaskConcrete                1.513e-01  1.239e-02  2.127e+04  12.204  < 2e-16
## TaskValence                -4.425e-02  9.268e-03  2.121e+04  -4.774 1.82e-06
## CategoryNouns               2.510e-02  4.589e-02  9.925e+01   0.547    0.586
## CategoryVerbs               8.471e-02  5.714e-02  9.951e+01   1.482    0.141
## TaskConcrete:CategoryNouns -1.018e-01  1.070e-02  2.124e+04  -9.519  < 2e-16
## TaskConcrete:CategoryVerbs -5.250e-02  1.231e-02  2.116e+04  -4.264 2.02e-05
##                               
## (Intercept)                ***
## TaskConcrete               ***
## TaskValence                ***
## CategoryNouns                 
## CategoryVerbs                 
## TaskConcrete:CategoryNouns ***
## TaskConcrete:CategoryVerbs ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) TskCnc TskVln CtgryN CtgryV TsC:CN
## TaskConcret -0.244                                   
## TaskValence -0.238  0.748                            
## CategoryNns -0.810  0.083  0.035                     
## CategryVrbs -0.644  0.045  0.000  0.549              
## TskCncrt:CN  0.078 -0.513 -0.003 -0.096 -0.052       
## TskCncrt:CV  0.067 -0.444  0.000 -0.057 -0.104  0.514
## fit warnings:
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients

In the Concreteness task, is there a difference between concreteness and abstractness on ReactionTime?

• Nope

str(df_factors)

## tibble [21,423 × 23] (S3: tbl_df/tbl/data.frame)
##  $ ...1           : Factor w/ 5014 levels "1","2","3","4",..: 1 3 4 5 6 7 8 9 10 11 ...
##  $ ID.true        : Factor w/ 95 levels "55132578fdf99b1b0568bdb8",..: 13 13 13 13 13 13 13 13 13 13 ...
##  $ Word           : Factor w/ 226 levels "admired","annoy",..: 129 176 2 22 182 11 144 199 119 217 ...
##  $ Label          : Factor w/ 3 levels "test_ani","test_conc",..: 3 3 3 3 3 3 3 3 3 3 ...
##  $ ConcValCombo   : Factor w/ 4 levels "abstract-negative",..: 1 4 1 4 1 1 3 3 2 3 ...
##  $ Task           : Factor w/ 3 levels "Animacy","Concrete",..: 3 3 3 3 3 3 3 3 3 3 ...
##  $ BlockOrder     : Factor w/ 2 levels "CV","VC": 2 2 2 2 2 2 2 2 2 2 ...
##  $ Group          : Factor w/ 6 levels "A","abstract;concrete",..: 3 3 3 3 3 3 3 3 3 3 ...
##  $ Response       : Factor w/ 6 levels "abstract","animate",..: 5 6 5 6 5 5 5 5 6 5 ...
##  $ Accuracy       : Factor w/ 1 level "1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ EventTime      : Factor w/ 21360 levels "1732203168805",..: 3704 3705 3706 3707 3708 3709 3710 3711 3712 3713 ...
##  $ Value          : Factor w/ 6 levels "abstract","animate",..: 5 6 5 6 5 5 5 5 6 5 ...
##  $ RT             : Factor w/ 2641 levels "1231.66666666667",..: 1827 2641 2151 2589 2246 2394 2121 2109 1798 1831 ...
##  $ ReactionTime   : num [1:21423] 1164 1184 1660 1157 1264 ...
##  $ Key_value_F    : Factor w/ 6 levels "A","abstract",..: 5 5 5 5 5 5 5 5 5 5 ...
##  $ Key_value_J    : Factor w/ 6 levels "A","animate;inanimate",..: 3 3 3 3 3 3 3 3 3 3 ...
##  $ Comments       : Factor w/ 0 levels: NA NA NA NA NA NA NA NA NA NA ...
##  $ LogReactionTime: num [1:21423] 7.06 7.08 7.41 7.05 7.14 ...
##  $ LogRT          : Factor w/ 2641 levels "7.11612354471419",..: 1827 2641 2151 2589 2246 2394 2121 2109 1798 1831 ...
##  $ TrialNumber    : Factor w/ 252 levels "1","2","3","4",..: 1 3 4 5 6 7 8 9 10 11 ...
##  $ Category       : Factor w/ 3 levels "Adjs","Nouns",..: 3 3 3 3 3 3 3 3 3 3 ...
##  $ Animacy        : Factor w/ 2 levels "animate","inanimate": NA NA NA NA NA NA NA NA NA NA ...
##  $ Valence        : Factor w/ 2 levels "negative","positive": NA NA NA NA NA NA NA NA NA NA ...

conc <- df_factors %>% 
  filter(Task == "Concrete") %>% 
  mutate(Response.n = as.numeric(factor(Response, levels = c("abstract", "concrete"))) - 1,
         Concrete = ifelse(grepl("abstract", ConcValCombo), "abstract", 
                    ifelse(grepl("concrete", ConcValCombo), "concrete", NA)),
        cConcValCombo = as.numeric(ConcValCombo) - mean(as.numeric(ConcValCombo)),
        cConcrete = as.numeric(as.factor(Concrete)) - mean(as.numeric(as.factor(Concrete))),
        # cSyntactic = as.numeric(factor(Syntactic)) - mean(as.numeric(factor(Syntactic)))
  )

m = lmer(LogReactionTime ~ cConcValCombo + (1+cConcValCombo|ID.true) + (1+cConcValCombo|Word), data=conc)
summary(m)

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: LogReactionTime ~ cConcValCombo + (1 + cConcValCombo | ID.true) +  
##     (1 + cConcValCombo | Word)
##    Data: conc
## 
## REML criterion at convergence: 4613.8
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.6323 -0.6449 -0.1766  0.4766  4.9365 
## 
## Random effects:
##  Groups   Name          Variance Std.Dev. Corr 
##  Word     (Intercept)   0.002498 0.04998       
##           cConcValCombo 0.002526 0.05026  0.82 
##  ID.true  (Intercept)   0.042551 0.20628       
##           cConcValCombo 0.002551 0.05051  -0.15
##  Residual               0.094262 0.30702       
## Number of obs: 8372, groups:  Word, 196; ID.true, 77
## 
## Fixed effects:
##                 Estimate Std. Error         df t value Pr(>|t|)    
## (Intercept)    6.790e+00  2.426e-02  8.224e+01 279.860   <2e-16 ***
## cConcValCombo -7.517e-04  8.212e-03  1.215e+02  -0.092    0.927    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr)
## cConcValCmb -0.019

No effect of word concreteness

m = lmer(LogReactionTime ~ cConcrete + (1+cConcrete|ID.true) + (1|Word), data=conc)
summary(m)

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: LogReactionTime ~ cConcrete + (1 + cConcrete | ID.true) + (1 |  
##     Word)
##    Data: conc
## 
## REML criterion at convergence: 4591.7
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.6472 -0.6501 -0.1797  0.4772  5.0350 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev. Corr 
##  Word     (Intercept) 0.004929 0.07021       
##  ID.true  (Intercept) 0.042413 0.20594       
##           cConcrete   0.014480 0.12033  -0.14
##  Residual             0.093722 0.30614       
## Number of obs: 8372, groups:  Word, 196; ID.true, 77
## 
## Fixed effects:
##              Estimate Std. Error        df t value Pr(>|t|)    
## (Intercept)   6.78054    0.02427  82.89093  279.43   <2e-16 ***
## cConcrete    -0.02351    0.01836 126.50710   -1.28    0.203    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##           (Intr)
## cConcrete -0.104

Small effect of response choice

m = lmer(LogReactionTime ~ Response.n + (1|ID.true) + (1|Word), data=conc)
summary(m)

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: LogReactionTime ~ Response.n + (1 | ID.true) + (1 | Word)
##    Data: conc
## 
## REML criterion at convergence: 4749.9
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.4064 -0.6558 -0.1794  0.4899  4.9799 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  Word     (Intercept) 0.005499 0.07416 
##  ID.true  (Intercept) 0.042090 0.20516 
##  Residual             0.096810 0.31114 
## Number of obs: 8372, groups:  Word, 196; ID.true, 77
## 
## Fixed effects:
##              Estimate Std. Error        df t value Pr(>|t|)    
## (Intercept)   6.78847    0.02502  94.43954 271.289   <2e-16 ***
## Response.n   -0.02150    0.01262 185.60793  -1.704   0.0901 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##            (Intr)
## Response.n -0.247

In the Valence task , is there a difference between positive and negative on ReactionTime?

• Nope.

val <- df.outliers.removed %>% 
  filter(Task == "Valence") %>%
  filter(!is.na(ConcValCombo)) %>% 
  mutate(Response.n = as.numeric(factor(Response, levels = c("negative", "positive"))) - 1,
         Valence = case_when(
                        grepl("negative", ConcValCombo) ~ "negative",
                        grepl("positive", ConcValCombo) ~ "positive",
                        TRUE ~ NA_character_
                    ),
         # Valence = ifelse(grepl("negative", ConcValCombo), "negative", 
         #            ifelse(grepl("positive", ConcValCombo), "positive", NA)),
        cConcValCombo = as.numeric(as.factor(ConcValCombo)) - mean(as.numeric(as.factor(ConcValCombo))),
        cValence = as.numeric(as.factor(Valence)) - mean(as.numeric(as.factor(Valence)))
  )


sum(is.na(val$ConcValCombo))  # Count missing values

## [1] 0

sum(is.na(val$LogReactionTime)) # Check for missing LogReactionTime

## [1] 0

var(val$cConcValCombo)

## [1] 1.246584

unique(val$ConcValCombo)

## [1] "abstract-negative" "concrete-positive" "concrete-negative"
## [4] "abstract-positive"

# View(val)
# valna <- val %>% 
#   filter(is.na(val$ConcValCombo))

m = lmer(LogReactionTime ~ cConcValCombo + (1+cConcValCombo|ID.true) + (1+cConcValCombo|Word), data=val)

## boundary (singular) fit: see help('isSingular')

summary(m)

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: LogReactionTime ~ cConcValCombo + (1 + cConcValCombo | ID.true) +  
##     (1 + cConcValCombo | Word)
##    Data: val
## 
## REML criterion at convergence: 1853
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.0824 -0.6201 -0.1528  0.4390  5.7738 
## 
## Random effects:
##  Groups   Name          Variance  Std.Dev. Corr 
##  Word     (Intercept)   3.730e-03 0.061070      
##           cConcValCombo 1.093e-06 0.001046 -1.00
##  ID.true  (Intercept)   4.138e-02 0.203412      
##           cConcValCombo 3.431e-04 0.018524 0.05 
##  Residual               6.725e-02 0.259324      
## Number of obs: 8907, groups:  Word, 196; ID.true, 77
## 
## Fixed effects:
##                 Estimate Std. Error         df t value Pr(>|t|)    
## (Intercept)     6.638771   0.023771  81.083818 279.280   <2e-16 ***
## cConcValCombo  -0.005728   0.005051 167.525286  -1.134    0.258    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr)
## cConcValCmb 0.016 
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')

Effect of Word Valence

m = lmer(LogReactionTime ~ cValence + (1+cValence|ID.true) + (1|Word), data=val)
summary(m)

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: LogReactionTime ~ cValence + (1 + cValence | ID.true) + (1 |  
##     Word)
##    Data: val
## 
## REML criterion at convergence: 1809.5
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.2369 -0.6191 -0.1465  0.4441  5.8113 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev. Corr
##  Word     (Intercept) 0.003671 0.06058      
##  ID.true  (Intercept) 0.041438 0.20356      
##           cValence    0.003828 0.06187  0.04
##  Residual             0.066700 0.25826      
## Number of obs: 8907, groups:  Word, 196; ID.true, 77
## 
## Fixed effects:
##              Estimate Std. Error        df t value Pr(>|t|)    
## (Intercept)   6.63902    0.02378  81.01248 279.187   <2e-16 ***
## cValence     -0.02925    0.01254 160.56427  -2.332   0.0209 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##          (Intr)
## cValence 0.020

Effect of response choice

m = lmer(LogReactionTime ~ Response.n + (1|ID.true) + (1|Word), data=val)
summary(m)

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: LogReactionTime ~ Response.n + (1 | ID.true) + (1 | Word)
##    Data: val
## 
## REML criterion at convergence: 1858.1
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.0661 -0.6212 -0.1492  0.4456  5.7690 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  Word     (Intercept) 0.003586 0.05988 
##  ID.true  (Intercept) 0.041369 0.20339 
##  Residual             0.067655 0.26011 
## Number of obs: 8907, groups:  Word, 196; ID.true, 77
## 
## Fixed effects:
##              Estimate Std. Error        df t value Pr(>|t|)    
## (Intercept)   6.65332    0.02430  88.34958 273.812  < 2e-16 ***
## Response.n   -0.02941    0.01026 184.41563  -2.866  0.00464 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##            (Intr)
## Response.n -0.211

In the Animacy task , is there a difference between positive and negative on ReactionTime?

• Nope.

ani <- df_factors %>% 
  filter(Task == "Animacy") %>% 
  mutate(Response.n = as.numeric(factor(Response, levels = c("inanimate", "animate"))) - 1,
         cAnimacy = as.numeric(Animacy) - mean(as.numeric(Animacy)))

Effect of word animacy

m = lmer(LogReactionTime ~ cAnimacy + (1+cAnimacy|ID.true) + (1|Word), data=ani)
summary(m)

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: LogReactionTime ~ cAnimacy + (1 + cAnimacy | ID.true) + (1 |  
##     Word)
##    Data: ani
## 
## REML criterion at convergence: 1148.7
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.3632 -0.6116 -0.1985  0.4071  5.1028 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev. Corr
##  Word     (Intercept) 0.007883 0.08879      
##  ID.true  (Intercept) 0.021658 0.14717      
##           cAnimacy    0.006590 0.08118  0.84
##  Residual             0.095076 0.30834      
## Number of obs: 2079, groups:  Word, 40; ID.true, 18
## 
## Fixed effects:
##             Estimate Std. Error       df t value Pr(>|t|)    
## (Intercept)  6.62994    0.03804 22.39932 174.283   <2e-16 ***
## cAnimacy     0.09208    0.03662 40.94232   2.514    0.016 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##          (Intr)
## cAnimacy 0.408

No effect of response choice

m = lmer(LogReactionTime ~ Response.n + (1|ID.true) + (1|Word), data=ani)
summary(m)

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: LogReactionTime ~ Response.n + (1 | ID.true) + (1 | Word)
##    Data: ani
## 
## REML criterion at convergence: 1181.9
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.2693 -0.6231 -0.2037  0.3908  5.0174 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  Word     (Intercept) 0.009672 0.09834 
##  ID.true  (Intercept) 0.021860 0.14785 
##  Residual             0.096714 0.31099 
## Number of obs: 2079, groups:  Word, 40; ID.true, 18
## 
## Fixed effects:
##               Estimate Std. Error         df t value Pr(>|t|)    
## (Intercept)   6.632630   0.041312  29.609700 160.549   <2e-16 ***
## Response.n   -0.008296   0.026878 110.576537  -0.309    0.758    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##            (Intr)
## Response.n -0.345